1. Technical Field
The present invention is directed to a method and apparatus for determining time varying thresholds for monitored metrics. In particular, the present invention is directed to a method and apparatus in which a neural network is used to predict values of metrics and thresholds for the metrics at various times are determined based on the predicted value of the metric at that time.
2. Description of Related Art
Monitoring computer system or network metrics is a complex task, particularly if metrics have significant time-varying behavior. Time varying-behavior makes the configuration of appropriate threshold values for metrics difficult. That is, because the behavior of the computing system or network changes from time to time, the use of the same threshold for determining improper operation of the system for all time periods will lead to reporting of false errors.
The most common approach to alleviating this problem is to use multiple fixed threshold values, each hand-configured to be valid for some period of time deemed significant by an administrator. When a metric's monitored value violates a threshold, an event is typically generated to notify administrators of an error condition.
While this approach is simple and widely used, it has significant drawbacks. The logic required to ignore all but the most important threshold violation is complicated, often resulting in multiple events reaching event consoles. This forces administrators to learn to ignore certain events in the presence of other events. The knowledge required to configure thresholds is also significant and a trial-and-error approach is often utilized.
Thus, it would be beneficial to have an apparatus and method for automatically determining thresholds for measured metrics in a time-varying manner. Further, it would be beneficial to have an apparatus and method for determining thresholds in a manner that reflects the seasonality of the metrics and their time-varying characteristics.